Extending the Foundational Model of Anatomy with Automatically Acquired Spatial Relations

Formal ontologies have made significant impact in bioscience over the last ten years. Among them, the Foundational Model of Anatomy Ontology (FMA) is the most comprehensive model for the spatio-structural representation of human anatomy. In the research project MEDICO we use the FMA as our main source of background knowledge about human anatomy. Our ultimate goals are to use spatial knowledge from the FMA (1) to improve automatic parsing algorithms for 3D volume data sets generated by Computed Tomography and Magnetic Resonance Imaging and (2) to generate semantic annotations using the concepts from the FMA to allow semantic search on medical image repositories. We argue that in this context more spatial relation instances are needed than those currently available in the FMA. In this publication we present a technique for the automatic inductive acquisition of spatial relation instances by generalizing from expert-annotated volume datasets

Continuous Improvement Through Knowledge-Guided Analysis in Experience Feedback

Continuous improvement in industrial processes is increasingly a key element of competitiveness for industrial systems. The management of experience feedback in this framework is designed to build, analyze and facilitate the knowledge sharing among problem solving practitioners of an organization in order to improve processes and products achievement. During Problem Solving Processes, the intellectual investment of experts is often considerable and the opportunities for expert knowledge exploitation are numerous: decision making, problem solving under uncertainty, and expert configuration. In this paper, our contribution relates to the structuring of a cognitive experience feedback framework, which allows a flexible exploitation of expert knowledge during Problem Solving Processes and a reuse such collected experience. To that purpose, the proposed approach uses the general principles of root cause analysis for identifying the root causes of problems or events, the conceptual graphs formalism for the semantic conceptualization of the domain vocabulary and the Transferable Belief Model for the fusion of information from different sources. The underlying formal reasoning mechanisms (logic-based semantics) in conceptual graphs enable intelligent information retrieval for the effective exploitation of lessons learned from past projects. An example will illustrate the application of the proposed approach of experience feedback processes formalization in the transport industry sector

Ontology-based specific and exhaustive user profiles for constraint information fusion for multi-agents

Intelligent agents are an advanced technology utilized in Web Intelligence. When searching information from a distributed Web environment, information is retrieved by multi-agents on the client site and fused on the broker site. The current information fusion techniques rely on cooperation of agents to provide statistics. Such techniques are computationally expensive and unrealistic in the real world. In this paper, we introduce a model that uses a world ontology constructed from the Dewey Decimal Classification to acquire user profiles. By search using specific and exhaustive user profiles, information fusion techniques no longer rely on the statistics provided by agents. The model has been successfully evaluated using the large INEX data set simulating the distributed Web environment

A Conceptual Representation of Documents and Queries for Information Retrieval Systems by Using Light Ontologies

International audienceThis article presents a vector space model approach to representing documents and queries, based on concepts instead of terms and using WordNet as a light ontology. Such representation reduces information overlap with respect to classic semantic expansion techniques. Experiments carried out on the MuchMore benchmark and on the TREC-7 and TREC-8 Ad-hoc collections demonstrate the effectiveness of the proposed approach